Abstract: Molecular targeting with exogenous near-infrared excitable fluorescent agents using time-dependent imaging techniques may enable diagnostic imaging of breast cancer and prognostic imaging of sentinel lymph nodes within the breast. However, prior to the administration of unproven contrast agents, phantom studies on clinically relevant volumes are essential to assess the benefits of fluorescence-enhanced optical imaging in humans. Diagnostic 3-D fluorescence-enhanced optical tomography is demonstrated using 0.5 to 1 cm^3 single and multiple targets differentiated from their surroundings by indocyanine green (micromolar) in a breast-shaped phantom (10-cm diameter). Fluorescence measurements of referenced ac intensity and phase shift were acquired in response to point illumination measurement geometry using a homodyned intensified charge-coupled device system modulated at 100 MHz. Bayesian reconstructions show artifact-free 3-D images (3857 unknowns) from 3-D boundary surface measurements (126 to 439). In a reflectance geometry appropriate for prognostic imaging of lymph node involvement, fluorescence measurements were likewise acquired from the surface of a semi-infinite phantom (8*8*8 cm^3) in response to area illumination (12 cm^2) by excitation light. Tomographic 3-D reconstructions (24,123 unknowns) were recovered from 2-D boundary surface measurements (3194) using the modified truncated Newton’s method. These studies represent the first 3-D tomographic images from physiologically relevant geometries for breast imaging.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).